Profiling Report

Dataset statistics

Number of variables	40
Number of observations	63697
Missing cells	87873
Missing cells (%)	3.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	19.4 MiB
Average record size in memory	320.0 B

Variable types

Categorical	23
Numeric	17

Alerts

`client_id` has a high cardinality: 63697 distinct values	High cardinality
`customer_since_all` has a high cardinality: 661 distinct values	High cardinality
`customer_since_bank` has a high cardinality: 577 distinct values	High cardinality
`customer_birth_date` has a high cardinality: 1092 distinct values	High cardinality
`homebanking_active` is highly correlated with `has_homebanking`	High correlation
`has_homebanking` is highly correlated with `homebanking_active` and 1 other fields	High correlation
`has_insurance_21` is highly correlated with `bal_insurance_21`	High correlation
`has_insurance_23` is highly correlated with `bal_insurance_23`	High correlation
`has_life_insurance_fixed_cap` is highly correlated with `cap_life_insurance_fixed_cap`	High correlation
`has_life_insurance_decreasing_cap` is highly correlated with `has_mortgage_loan` and 2 other fields	High correlation
`has_fire_car_other_insurance` is highly correlated with `prem_fire_car_other_insurance`	High correlation
`has_personal_loan` is highly correlated with `bal_personal_loan`	High correlation
`has_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`has_current_account` is highly correlated with `has_homebanking` and 1 other fields	High correlation
`has_pension_saving` is highly correlated with `bal_pension_saving`	High correlation
`has_savings_account_starter` is highly correlated with `bal_savings_account_starter`	High correlation
`has_current_account_starter` is highly correlated with `bal_current_account_starter`	High correlation
`bal_insurance_21` is highly correlated with `has_insurance_21`	High correlation
`bal_insurance_23` is highly correlated with `has_insurance_23`	High correlation
`cap_life_insurance_fixed_cap` is highly correlated with `has_life_insurance_fixed_cap`	High correlation
`cap_life_insurance_decreasing_cap` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`prem_fire_car_other_insurance` is highly correlated with `has_fire_car_other_insurance`	High correlation
`bal_personal_loan` is highly correlated with `has_personal_loan`	High correlation
`bal_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`bal_current_account` is highly correlated with `has_current_account`	High correlation
`bal_pension_saving` is highly correlated with `has_pension_saving`	High correlation
`bal_savings_account_starter` is highly correlated with `has_savings_account_starter`	High correlation
`bal_current_account_starter` is highly correlated with `has_current_account_starter`	High correlation
`homebanking_active` is highly correlated with `has_homebanking`	High correlation
`has_homebanking` is highly correlated with `homebanking_active` and 1 other fields	High correlation
`has_insurance_21` is highly correlated with `bal_insurance_21`	High correlation
`has_insurance_23` is highly correlated with `bal_insurance_23`	High correlation
`has_life_insurance_fixed_cap` is highly correlated with `cap_life_insurance_fixed_cap`	High correlation
`has_life_insurance_decreasing_cap` is highly correlated with `has_mortgage_loan` and 2 other fields	High correlation
`has_fire_car_other_insurance` is highly correlated with `prem_fire_car_other_insurance`	High correlation
`has_personal_loan` is highly correlated with `bal_personal_loan`	High correlation
`has_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`has_current_account` is highly correlated with `has_homebanking`	High correlation
`has_pension_saving` is highly correlated with `bal_pension_saving`	High correlation
`has_savings_account` is highly correlated with `bal_current_account`	High correlation
`has_savings_account_starter` is highly correlated with `bal_savings_account_starter`	High correlation
`has_current_account_starter` is highly correlated with `bal_current_account_starter`	High correlation
`bal_insurance_21` is highly correlated with `has_insurance_21`	High correlation
`bal_insurance_23` is highly correlated with `has_insurance_23`	High correlation
`cap_life_insurance_fixed_cap` is highly correlated with `has_life_insurance_fixed_cap`	High correlation
`cap_life_insurance_decreasing_cap` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`prem_fire_car_other_insurance` is highly correlated with `has_fire_car_other_insurance`	High correlation
`bal_personal_loan` is highly correlated with `has_personal_loan`	High correlation
`bal_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`bal_current_account` is highly correlated with `has_savings_account`	High correlation
`bal_pension_saving` is highly correlated with `has_pension_saving`	High correlation
`bal_savings_account_starter` is highly correlated with `has_savings_account_starter`	High correlation
`bal_current_account_starter` is highly correlated with `has_current_account_starter`	High correlation
`visits_distinct_so` is highly correlated with `visits_distinct_so_areas`	High correlation
`visits_distinct_so_areas` is highly correlated with `visits_distinct_so`	High correlation
`homebanking_active` is highly correlated with `has_homebanking`	High correlation
`has_homebanking` is highly correlated with `homebanking_active` and 1 other fields	High correlation
`has_insurance_21` is highly correlated with `bal_insurance_21`	High correlation
`has_insurance_23` is highly correlated with `bal_insurance_23`	High correlation
`has_life_insurance_fixed_cap` is highly correlated with `cap_life_insurance_fixed_cap`	High correlation
`has_life_insurance_decreasing_cap` is highly correlated with `has_mortgage_loan` and 2 other fields	High correlation
`has_fire_car_other_insurance` is highly correlated with `prem_fire_car_other_insurance`	High correlation
`has_personal_loan` is highly correlated with `bal_personal_loan`	High correlation
`has_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`has_current_account` is highly correlated with `has_homebanking` and 1 other fields	High correlation
`has_pension_saving` is highly correlated with `bal_pension_saving`	High correlation
`has_savings_account_starter` is highly correlated with `bal_savings_account_starter`	High correlation
`has_current_account_starter` is highly correlated with `bal_current_account_starter`	High correlation
`bal_insurance_21` is highly correlated with `has_insurance_21`	High correlation
`bal_insurance_23` is highly correlated with `has_insurance_23`	High correlation
`cap_life_insurance_fixed_cap` is highly correlated with `has_life_insurance_fixed_cap`	High correlation
`cap_life_insurance_decreasing_cap` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`prem_fire_car_other_insurance` is highly correlated with `has_fire_car_other_insurance`	High correlation
`bal_personal_loan` is highly correlated with `has_personal_loan`	High correlation
`bal_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`bal_current_account` is highly correlated with `has_current_account`	High correlation
`bal_pension_saving` is highly correlated with `has_pension_saving`	High correlation
`bal_savings_account_starter` is highly correlated with `has_savings_account_starter`	High correlation
`bal_current_account_starter` is highly correlated with `has_current_account_starter`	High correlation
`customer_relationship` is highly correlated with `customer_children`	High correlation
`has_homebanking` is highly correlated with `homebanking_active` and 1 other fields	High correlation
`has_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap`	High correlation
`has_life_insurance_decreasing_cap` is highly correlated with `has_mortgage_loan`	High correlation
`homebanking_active` is highly correlated with `has_homebanking`	High correlation
`customer_children` is highly correlated with `customer_relationship`	High correlation
`has_current_account` is highly correlated with `has_homebanking`	High correlation
`homebanking_active` is highly correlated with `has_homebanking` and 3 other fields	High correlation
`has_homebanking` is highly correlated with `homebanking_active` and 3 other fields	High correlation
`has_insurance_21` is highly correlated with `bal_insurance_21`	High correlation
`has_insurance_23` is highly correlated with `bal_insurance_23`	High correlation
`has_life_insurance_fixed_cap` is highly correlated with `cap_life_insurance_fixed_cap`	High correlation
`has_life_insurance_decreasing_cap` is highly correlated with `homebanking_active` and 4 other fields	High correlation
`has_fire_car_other_insurance` is highly correlated with `prem_fire_car_other_insurance`	High correlation
`has_personal_loan` is highly correlated with `bal_personal_loan`	High correlation
`has_mortgage_loan` is highly correlated with `homebanking_active` and 4 other fields	High correlation
`has_current_account` is highly correlated with `homebanking_active` and 2 other fields	High correlation
`has_pension_saving` is highly correlated with `bal_pension_saving`	High correlation
`has_savings_account` is highly correlated with `bal_current_account` and 1 other fields	High correlation
`has_savings_account_starter` is highly correlated with `bal_savings_account_starter`	High correlation
`has_current_account_starter` is highly correlated with `bal_current_account_starter`	High correlation
`bal_insurance_21` is highly correlated with `has_insurance_21`	High correlation
`bal_insurance_23` is highly correlated with `has_insurance_23`	High correlation
`cap_life_insurance_fixed_cap` is highly correlated with `has_life_insurance_fixed_cap`	High correlation
`cap_life_insurance_decreasing_cap` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`prem_fire_car_other_insurance` is highly correlated with `has_fire_car_other_insurance`	High correlation
`bal_personal_loan` is highly correlated with `has_personal_loan`	High correlation
`bal_mortgage_loan` is highly correlated with `has_life_insurance_decreasing_cap` and 2 other fields	High correlation
`bal_current_account` is highly correlated with `has_current_account` and 2 other fields	High correlation
`bal_pension_saving` is highly correlated with `has_pension_saving`	High correlation
`bal_savings_account` is highly correlated with `has_savings_account` and 1 other fields	High correlation
`bal_savings_account_starter` is highly correlated with `has_savings_account_starter`	High correlation
`bal_current_account_starter` is highly correlated with `has_current_account_starter`	High correlation
`visits_distinct_so` is highly correlated with `visits_distinct_so_areas`	High correlation
`visits_distinct_so_areas` is highly correlated with `visits_distinct_so`	High correlation
`customer_occupation_code` is highly correlated with `customer_self_employed`	High correlation
`customer_self_employed` is highly correlated with `customer_occupation_code`	High correlation
`customer_occupation_code` has 2002 (3.1%) missing values	Missing
`customer_education` has 47125 (74.0%) missing values	Missing
`customer_children` has 23364 (36.7%) missing values	Missing
`customer_relationship` has 14899 (23.4%) missing values	Missing
`cap_life_insurance_fixed_cap` is highly skewed (γ1 = 48.2726678)	Skewed
`bal_current_account_starter` is highly skewed (γ1 = 21.12371347)	Skewed
`client_id` is uniformly distributed	Uniform
`client_id` has unique values	Unique
`bal_insurance_21` has 57719 (90.6%) zeros	Zeros
`bal_insurance_23` has 63083 (99.0%) zeros	Zeros
`cap_life_insurance_fixed_cap` has 63522 (99.7%) zeros	Zeros
`cap_life_insurance_decreasing_cap` has 56589 (88.8%) zeros	Zeros
`prem_fire_car_other_insurance` has 43753 (68.7%) zeros	Zeros
`bal_personal_loan` has 61047 (95.8%) zeros	Zeros
`bal_mortgage_loan` has 57449 (90.2%) zeros	Zeros
`bal_current_account` has 36146 (56.7%) zeros	Zeros
`bal_pension_saving` has 62389 (97.9%) zeros	Zeros
`bal_savings_account` has 1930 (3.0%) zeros	Zeros
`bal_savings_account_starter` has 63326 (99.4%) zeros	Zeros
`bal_current_account_starter` has 62798 (98.6%) zeros	Zeros
`customer_education` has 2178 (3.4%) zeros	Zeros

Reproduction

Analysis started	2022-03-19 23:50:00.034799
Analysis finished	2022-03-19 23:51:34.248842
Duration	1 minute and 34.21 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

client_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	63697
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

910df42ad36243aa4ce16324cd7b15b0	1
430928063ed9e6128bf0843c1b76d655	1
8a035800d386ca328e42f91f3bd34f46	1
1cec47030db37dc49b734bad4b62cc15	1
70db2ef12ad7754c4cad43aff36397d5	1
Other values (63692)	63692

Length

Max length	32
Median length	32
Mean length	32
Min length	32

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	63697 ?
Unique (%)	100.0%

Sample

1st row	910df42ad36243aa4ce16324cd7b15b0
2nd row	4e19dc3a54323c5bbfc374664b950cd1
3rd row	f5d08db1b86c0cb0f566bf446cff1fb4
4th row	26170ecf63653e215c52f4262c1c4859
5th row	c078009957dffb64f20e61b41220a976

Common Values

Value	Count	Frequency (%)
910df42ad36243aa4ce16324cd7b15b0	1	< 0.1%
430928063ed9e6128bf0843c1b76d655	1	< 0.1%
8a035800d386ca328e42f91f3bd34f46	1	< 0.1%
1cec47030db37dc49b734bad4b62cc15	1	< 0.1%
70db2ef12ad7754c4cad43aff36397d5	1	< 0.1%
7c3db4bc035ef61b497b45a170b28d70	1	< 0.1%
f07ce1239d3f86d60899b19a2d6415b5	1	< 0.1%
41a5faa1412905468343e9236320f166	1	< 0.1%
049cb59ce12bd1f7bc85d3b35450f8d7	1	< 0.1%
e94231bda207b0c8e30a8b47f194c3dc	1	< 0.1%
Other values (63687)	63687	> 99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
910df42ad36243aa4ce16324cd7b15b0	1	< 0.1%
ebbf9840c425d75aa856d8d2f070bc0d	1	< 0.1%
db53513a073326fb3d3cc256e3df3f47	1	< 0.1%
d68e579f5c95c38413b821533d96bdc9	1	< 0.1%
f5d08db1b86c0cb0f566bf446cff1fb4	1	< 0.1%
26170ecf63653e215c52f4262c1c4859	1	< 0.1%
c078009957dffb64f20e61b41220a976	1	< 0.1%
f7bae3a0fefd323ecf7d4a2fab4e7826	1	< 0.1%
e1b2293260bd55bc20700f122d70480f	1	< 0.1%
2063dc028a989d9752b9565bd42a4eff	1	< 0.1%
Other values (63687)	63687	> 99.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

homebanking_active
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	49990
1	13707

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	49990	78.5%
1	13707	21.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	49990	78.5%
1	13707	21.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_homebanking
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	45802
1	17895

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	45802	71.9%
1	17895	28.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	45802	71.9%
1	17895	28.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_insurance_21
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	57644
1	6053

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	57644	90.5%
1	6053	9.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	57644	90.5%
1	6053	9.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_insurance_23
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	63063
1	634

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	63063	99.0%
1	634	1.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	63063	99.0%
1	634	1.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_life_insurance_fixed_cap
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	63522
1	175

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	63522	99.7%
1	175	0.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	63522	99.7%
1	175	0.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_life_insurance_decreasing_cap
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	56577
1	7120

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	56577	88.8%
1	7120	11.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	56577	88.8%
1	7120	11.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_fire_car_other_insurance
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	43438
1	20259

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	43438	68.2%
1	20259	31.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	43438	68.2%
1	20259	31.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_personal_loan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	61046
1	2651

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	61046	95.8%
1	2651	4.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	61046	95.8%
1	2651	4.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_mortgage_loan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	57449
1	6248

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	57449	90.2%
1	6248	9.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	57449	90.2%
1	6248	9.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_current_account
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

1	31900
0	31797

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
1	31900	50.1%
0	31797	49.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	31900	50.1%
0	31797	49.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_pension_saving
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	62298
1	1399

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	62298	97.8%
1	1399	2.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	62298	97.8%
1	1399	2.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_savings_account
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

1	61847
0	1850

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	61847	97.1%
0	1850	2.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	61847	97.1%
0	1850	2.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_savings_account_starter
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	63320
1	377

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	63320	99.4%
1	377	0.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	63320	99.4%
1	377	0.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

has_current_account_starter
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	62553
1	1144

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	62553	98.2%
1	1144	1.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	62553	98.2%
1	1144	1.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

bal_insurance_21
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	987
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	457.9601865

Minimum	0
Maximum	10000
Zeros	57719
Zeros (%)	90.6%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	4610
Maximum	10000
Range	10000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1641.970743
Coefficient of variation (CV)	3.585400634
Kurtosis	14.28661532
Mean	457.9601865
Median Absolute Deviation (MAD)	0
Skewness	3.846945482
Sum	29170690
Variance	2696067.921
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	57719	90.6%
1120	28	< 0.1%
2710	22	< 0.1%
2580	22	< 0.1%
2680	21	< 0.1%
1710	21	< 0.1%
2660	19	< 0.1%
2820	18	< 0.1%
2830	17	< 0.1%
2620	17	< 0.1%
Other values (977)	5793	9.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	57719	90.6%
10	1	< 0.1%
20	4	< 0.1%
30	3	< 0.1%
40	7	< 0.1%
50	5	< 0.1%
60	2	< 0.1%
70	13	< 0.1%
80	4	< 0.1%
90	4	< 0.1%

Value	Count	Frequency (%)
10000	1	< 0.1%
9990	2	< 0.1%
9970	2	< 0.1%
9960	3	< 0.1%
9950	2	< 0.1%
9940	4	< 0.1%
9930	3	< 0.1%
9920	1	< 0.1%
9900	6	< 0.1%
9890	3	< 0.1%

bal_insurance_23
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	399
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	47.43237515

Minimum	0
Maximum	9890
Zeros	63083
Zeros (%)	99.0%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	9890
Range	9890
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	536.9786303
Coefficient of variation (CV)	11.32093066
Kurtosis	180.0780522
Mean	47.43237515
Median Absolute Deviation (MAD)	0
Skewness	12.90420201
Sum	3021300
Variance	288346.0494
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	63083	99.0%
2520	9	< 0.1%
2580	9	< 0.1%
5070	8	< 0.1%
2540	6	< 0.1%
2510	6	< 0.1%
5020	5	< 0.1%
2480	5	< 0.1%
4610	5	< 0.1%
5150	5	< 0.1%
Other values (389)	556	0.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	63083	99.0%
90	1	< 0.1%
100	1	< 0.1%
110	1	< 0.1%
380	1	< 0.1%
600	1	< 0.1%
620	1	< 0.1%
670	1	< 0.1%
730	1	< 0.1%
750	1	< 0.1%

Value	Count	Frequency (%)
9890	1	< 0.1%
9880	1	< 0.1%
9870	2	< 0.1%
9860	3	< 0.1%
9850	1	< 0.1%
9840	1	< 0.1%
9830	2	< 0.1%
9790	1	< 0.1%
9780	2	< 0.1%
9770	1	< 0.1%

cap_life_insurance_fixed_cap
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	130
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	78.26004364

Minimum	0
Maximum	220000
Zeros	63522
Zeros (%)	99.7%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	220000
Range	220000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2538.927213
Coefficient of variation (CV)	32.4421901
Kurtosis	2875.610608
Mean	78.26004364
Median Absolute Deviation (MAD)	0
Skewness	48.2726678
Sum	4984930
Variance	6446151.395
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	63522	99.7%
50000	7	< 0.1%
40000	6	< 0.1%
100000	6	< 0.1%
5000	5	< 0.1%
3720	5	< 0.1%
2480	5	< 0.1%
10000	4	< 0.1%
24790	4	< 0.1%
2030	3	< 0.1%
Other values (120)	130	0.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	63522	99.7%
90	1	< 0.1%
250	1	< 0.1%
430	1	< 0.1%
460	1	< 0.1%
490	1	< 0.1%
530	1	< 0.1%
580	2	< 0.1%
590	1	< 0.1%
620	2	< 0.1%

Value	Count	Frequency (%)
220000	1	< 0.1%
200000	1	< 0.1%
171050	1	< 0.1%
150000	1	< 0.1%
140000	1	< 0.1%
127880	1	< 0.1%
125000	1	< 0.1%
117000	1	< 0.1%
104000	1	< 0.1%
100000	6	< 0.1%

cap_life_insurance_decreasing_cap
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	2032
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	11565.57059

Minimum	0
Maximum	780000
Zeros	56589
Zeros (%)	88.8%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	100000
Maximum	780000
Range	780000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	40229.76173
Coefficient of variation (CV)	3.478407002
Kurtosis	29.2956242
Mean	11565.57059
Median Absolute Deviation (MAD)	0
Skewness	4.63260835
Sum	736692150
Variance	1618433729
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	56589	88.8%
100000	234	0.4%
50000	173	0.3%
150000	162	0.3%
125000	156	0.2%
75000	143	0.2%
40000	137	0.2%
80000	128	0.2%
200000	113	0.2%
90000	101	0.2%
Other values (2022)	5761	9.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	56589	88.8%
310	1	< 0.1%
340	1	< 0.1%
480	1	< 0.1%
520	1	< 0.1%
530	1	< 0.1%
700	1	< 0.1%
900	1	< 0.1%
930	1	< 0.1%
960	1	< 0.1%

Value	Count	Frequency (%)
780000	1	< 0.1%
740000	2	< 0.1%
650000	1	< 0.1%
600000	1	< 0.1%
575500	1	< 0.1%
566500	1	< 0.1%
525000	1	< 0.1%
520000	1	< 0.1%
500000	1	< 0.1%
491000	1	< 0.1%

prem_fire_car_other_insurance
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	281
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	183.6670487

Minimum	0
Maximum	3000
Zeros	43753
Zeros (%)	68.7%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	240
95-th percentile	980
Maximum	3000
Range	3000
Interquartile range (IQR)	240

Descriptive statistics

Standard deviation	368.5996521
Coefficient of variation (CV)	2.006890483
Kurtosis	7.920696209
Mean	183.6670487
Median Absolute Deviation (MAD)	0
Skewness	2.604311701
Sum	11699040
Variance	135865.7035
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	43753	68.7%
60	421	0.7%
70	410	0.6%
90	399	0.6%
80	380	0.6%
380	305	0.5%
340	291	0.5%
370	289	0.5%
420	285	0.4%
330	282	0.4%
Other values (271)	16882	26.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	43753	68.7%
10	10	< 0.1%
20	44	0.1%
30	73	0.1%
40	64	0.1%
50	91	0.1%
60	421	0.7%
70	410	0.6%
80	380	0.6%
90	399	0.6%

Value	Count	Frequency (%)
3000	1	< 0.1%
2960	1	< 0.1%
2930	1	< 0.1%
2900	1	< 0.1%
2880	2	< 0.1%
2830	2	< 0.1%
2810	1	< 0.1%
2800	1	< 0.1%
2790	1	< 0.1%
2770	2	< 0.1%

bal_personal_loan
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	1512
Distinct (%)	2.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	402.4693471

Minimum	0
Maximum	71690
Zeros	61047
Zeros (%)	95.8%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	71690
Range	71690
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2662.560352
Coefficient of variation (CV)	6.615560591
Kurtosis	144.7193861
Mean	402.4693471
Median Absolute Deviation (MAD)	0
Skewness	10.31542097
Sum	25636090
Variance	7089227.626
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	61047	95.8%
920	8	< 0.1%
2750	7	< 0.1%
10790	7	< 0.1%
10000	7	< 0.1%
2520	7	< 0.1%
7230	7	< 0.1%
2580	7	< 0.1%
3910	7	< 0.1%
2770	6	< 0.1%
Other values (1502)	2587	4.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	61047	95.8%
110	3	< 0.1%
130	1	< 0.1%
150	3	< 0.1%
160	4	< 0.1%
170	1	< 0.1%
180	1	< 0.1%
190	2	< 0.1%
200	1	< 0.1%
210	1	< 0.1%

Value	Count	Frequency (%)
71690	1	< 0.1%
70400	1	< 0.1%
69160	1	< 0.1%
65810	1	< 0.1%
61070	1	< 0.1%
60330	1	< 0.1%
59960	1	< 0.1%
59030	1	< 0.1%
58410	1	< 0.1%
57120	1	< 0.1%

bal_mortgage_loan
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	5369
Distinct (%)	8.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	8868.815486

Minimum	0
Maximum	490000
Zeros	57449
Zeros (%)	90.2%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	71598
Maximum	490000
Range	490000
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	35525.2567
Coefficient of variation (CV)	4.00563714
Kurtosis	33.03241753
Mean	8868.815486
Median Absolute Deviation (MAD)	0
Skewness	5.245175199
Sum	564916940
Variance	1262043863
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	57449	90.2%
48940	5	< 0.1%
144240	5	< 0.1%
22420	4	< 0.1%
81130	4	< 0.1%
26880	4	< 0.1%
150000	4	< 0.1%
1190	4	< 0.1%
108570	4	< 0.1%
24230	4	< 0.1%
Other values (5359)	6210	9.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	57449	90.2%
140	1	< 0.1%
170	1	< 0.1%
200	1	< 0.1%
250	1	< 0.1%
260	1	< 0.1%
320	1	< 0.1%
330	2	< 0.1%
340	1	< 0.1%
350	1	< 0.1%

Value	Count	Frequency (%)
490000	1	< 0.1%
486990	1	< 0.1%
486620	1	< 0.1%
485040	1	< 0.1%
464150	1	< 0.1%
452020	1	< 0.1%
447720	1	< 0.1%
442480	1	< 0.1%
440360	1	< 0.1%
435430	1	< 0.1%

bal_current_account
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	1721
Distinct (%)	2.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1323.660612

Minimum	-1000
Maximum	20000
Zeros	36146
Zeros (%)	56.7%
Negative	348
Negative (%)	0.5%
Memory size	497.8 KiB

Quantile statistics

Minimum	-1000
5-th percentile	0
Q1	0
median	0
Q3	1520
95-th percentile	7140
Maximum	20000
Range	21000
Interquartile range (IQR)	1520

Descriptive statistics

Standard deviation	2685.727023
Coefficient of variation (CV)	2.029014839
Kurtosis	9.636095574
Mean	1323.660612
Median Absolute Deviation (MAD)	0
Skewness	2.911212365
Sum	84313210
Variance	7213129.643
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	36146	56.7%
10	330	0.5%
20	241	0.4%
60	193	0.3%
40	173	0.3%
50	173	0.3%
30	171	0.3%
80	162	0.3%
100	144	0.2%
70	131	0.2%
Other values (1711)	25833	40.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1000	1	< 0.1%
-950	2	< 0.1%
-940	1	< 0.1%
-930	1	< 0.1%
-920	1	< 0.1%
-890	1	< 0.1%
-880	1	< 0.1%
-850	2	< 0.1%
-840	1	< 0.1%
-820	1	< 0.1%

Value	Count	Frequency (%)
20000	1	< 0.1%
19860	1	< 0.1%
19840	1	< 0.1%
19800	1	< 0.1%
19790	1	< 0.1%
19690	1	< 0.1%
19550	1	< 0.1%
19360	1	< 0.1%
19350	1	< 0.1%
19280	1	< 0.1%

bal_pension_saving
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	934
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	233.4219822

Minimum	0
Maximum	41170
Zeros	62389
Zeros (%)	97.9%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	41170
Range	41170
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2140.173418
Coefficient of variation (CV)	9.168688391
Kurtosis	166.1891569
Mean	233.4219822
Median Absolute Deviation (MAD)	0
Skewness	12.07722778
Sum	14868280
Variance	4580342.26
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	62389	97.9%
6240	11	< 0.1%
6230	8	< 0.1%
4260	8	< 0.1%
910	7	< 0.1%
12110	7	< 0.1%
3210	6	< 0.1%
2210	6	< 0.1%
7120	6	< 0.1%
930	5	< 0.1%
Other values (924)	1244	2.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	62389	97.9%
120	1	< 0.1%
150	2	< 0.1%
160	1	< 0.1%
210	1	< 0.1%
230	3	< 0.1%
300	1	< 0.1%
310	1	< 0.1%
350	1	< 0.1%
380	2	< 0.1%

Value	Count	Frequency (%)
41170	1	< 0.1%
40600	1	< 0.1%
40510	1	< 0.1%
40430	1	< 0.1%
40320	1	< 0.1%
40230	1	< 0.1%
40110	1	< 0.1%
39940	1	< 0.1%
39850	1	< 0.1%
39750	1	< 0.1%

bal_savings_account
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	4936
Distinct (%)	7.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	17872.48489

Minimum	0
Maximum	50000
Zeros	1930
Zeros (%)	3.0%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	3638
Q1	8450
median	15170
Q3	25480
95-th percentile	41140
Maximum	50000
Range	50000
Interquartile range (IQR)	17030

Descriptive statistics

Standard deviation	11750.86847
Coefficient of variation (CV)	0.6574837544
Kurtosis	-0.3025699008
Mean	17872.48489
Median Absolute Deviation (MAD)	7890
Skewness	0.7195023378
Sum	1138423670
Variance	138082909.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1930	3.0%
15000	218	0.3%
25000	185	0.3%
10110	165	0.3%
20000	160	0.3%
10000	148	0.2%
10060	136	0.2%
5000	129	0.2%
10100	108	0.2%
30000	107	0.2%
Other values (4926)	60411	94.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1930	3.0%
10	25	< 0.1%
20	16	< 0.1%
30	10	< 0.1%
40	11	< 0.1%
50	6	< 0.1%
60	8	< 0.1%
70	6	< 0.1%
80	6	< 0.1%
90	6	< 0.1%

Value	Count	Frequency (%)
50000	79	0.1%
49990	1	< 0.1%
49970	3	< 0.1%
49960	6	< 0.1%
49950	1	< 0.1%
49940	2	< 0.1%
49930	2	< 0.1%
49900	3	< 0.1%
49890	1	< 0.1%
49870	1	< 0.1%

bal_savings_account_starter
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	334
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	57.64117619

Minimum	0
Maximum	24050
Zeros	63326
Zeros (%)	99.4%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	24050
Range	24050
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	892.9598587
Coefficient of variation (CV)	15.49170086
Kurtosis	363.4034392
Mean	57.64117619
Median Absolute Deviation (MAD)	0
Skewness	18.23536083
Sum	3671570
Variance	797377.3092
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	63326	99.4%
10000	4	< 0.1%
14000	3	< 0.1%
5010	3	< 0.1%
11000	3	< 0.1%
3000	3	< 0.1%
13410	3	< 0.1%
820	3	< 0.1%
7650	2	< 0.1%
15580	2	< 0.1%
Other values (324)	345	0.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	63326	99.4%
10	1	< 0.1%
70	1	< 0.1%
80	1	< 0.1%
120	1	< 0.1%
160	1	< 0.1%
260	1	< 0.1%
330	1	< 0.1%
520	1	< 0.1%
570	1	< 0.1%

Value	Count	Frequency (%)
24050	1	< 0.1%
24000	1	< 0.1%
23800	1	< 0.1%
23480	1	< 0.1%
23130	1	< 0.1%
22470	1	< 0.1%
22440	1	< 0.1%
22410	1	< 0.1%
22330	1	< 0.1%
22040	1	< 0.1%

bal_current_account_starter
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	414
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	30.32089423

Minimum	-330
Maximum	19790
Zeros	62798
Zeros (%)	98.6%
Negative	2
Negative (%)	< 0.1%
Memory size	497.8 KiB

Quantile statistics

Minimum	-330
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	19790
Range	20120
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	407.8778925
Coefficient of variation (CV)	13.45204034
Kurtosis	579.1917055
Mean	30.32089423
Median Absolute Deviation (MAD)	0
Skewness	21.12371347
Sum	1931350
Variance	166364.3752
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	62798	98.6%
10	29	< 0.1%
20	23	< 0.1%
50	16	< 0.1%
40	14	< 0.1%
30	12	< 0.1%
100	11	< 0.1%
80	9	< 0.1%
210	8	< 0.1%
120	8	< 0.1%
Other values (404)	769	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-330	1	< 0.1%
-170	1	< 0.1%
0	62798	98.6%
10	29	< 0.1%
20	23	< 0.1%
30	12	< 0.1%
40	14	< 0.1%
50	16	< 0.1%
60	7	< 0.1%
70	3	< 0.1%

Value	Count	Frequency (%)
19790	1	< 0.1%
17120	1	< 0.1%
15030	1	< 0.1%
14780	1	< 0.1%
14750	1	< 0.1%
14530	1	< 0.1%
14020	1	< 0.1%
13790	1	< 0.1%
13470	1	< 0.1%
12870	1	< 0.1%

visits_distinct_so
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	7
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.230199224

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	1
95-th percentile	2
Maximum	7
Range	6
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5014984575
Coefficient of variation (CV)	0.4076562947
Kurtosis	7.638635554
Mean	1.230199224
Median Absolute Deviation (MAD)	0
Skewness	2.459796736
Sum	78360
Variance	0.2515007029
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	51017	80.1%
2	11018	17.3%
3	1392	2.2%
4	229	0.4%
5	32	0.1%
6	8	< 0.1%
7	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	51017	80.1%
2	11018	17.3%
3	1392	2.2%
4	229	0.4%
5	32	0.1%
6	8	< 0.1%
7	1	< 0.1%

Value	Count	Frequency (%)
7	1	< 0.1%
6	8	< 0.1%
5	32	0.1%
4	229	0.4%
3	1392	2.2%
2	11018	17.3%
1	51017	80.1%

visits_distinct_so_areas
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	6
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.042607972

Minimum	1
Maximum	6
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	1
95-th percentile	1
Maximum	6
Range	5
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.2249913464
Coefficient of variation (CV)	0.2157966871
Kurtosis	51.61021735
Mean	1.042607972
Median Absolute Deviation (MAD)	0
Skewness	6.276163857
Sum	66411
Variance	0.05062110594
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
1	61246	96.2%
2	2227	3.5%
3	194	0.3%
4	23	< 0.1%
5	5	< 0.1%
6	2	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	61246	96.2%
2	2227	3.5%
3	194	0.3%
4	23	< 0.1%
5	5	< 0.1%
6	2	< 0.1%

Value	Count	Frequency (%)
6	2	< 0.1%
5	5	< 0.1%
4	23	< 0.1%
3	194	0.3%
2	2227	3.5%
1	61246	96.2%

customer_since_all
Categorical

HIGH CARDINALITY

Distinct	661
Distinct (%)	1.0%
Missing	234
Missing (%)	0.4%
Memory size	497.8 KiB

1981-01	3050
2017-02	744
2017-01	667
1994-06	610
2017-05	532
Other values (656)	57860

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	28 ?
Unique (%)	< 0.1%

Sample

1st row	1983-03
2nd row	2017-01
3rd row	1980-12
4th row	1998-08
5th row	2012-11

Common Values

Value	Count	Frequency (%)
1981-01	3050	4.8%
2017-02	744	1.2%
2017-01	667	1.0%
1994-06	610	1.0%
2017-05	532	0.8%
2016-10	486	0.8%
1995-04	457	0.7%
2002-11	422	0.7%
1994-07	421	0.7%
1991-12	404	0.6%
Other values (651)	55670	87.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
1981-01	3050	4.8%
2017-02	744	1.2%
2017-01	667	1.1%
1994-06	610	1.0%
2017-05	532	0.8%
2016-10	486	0.8%
1995-04	457	0.7%
2002-11	422	0.7%
1994-07	421	0.7%
1991-12	404	0.6%
Other values (651)	55670	87.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_since_bank
Categorical

HIGH CARDINALITY

Distinct	577
Distinct (%)	0.9%
Missing	249
Missing (%)	0.4%
Memory size	497.8 KiB

1981-01	3181
2017-02	1158
2017-01	1015
2017-05	794
2016-10	768
Other values (572)	56532

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10 ?
Unique (%)	< 0.1%

Sample

1st row	1994-08
2nd row	2017-01
3rd row	1980-12
4th row	2013-10
5th row	2012-11

Common Values

Value	Count	Frequency (%)
1981-01	3181	5.0%
2017-02	1158	1.8%
2017-01	1015	1.6%
2017-05	794	1.2%
2016-10	768	1.2%
1994-06	663	1.0%
2013-10	533	0.8%
2002-11	503	0.8%
1995-04	478	0.8%
1994-07	438	0.7%
Other values (567)	53917	84.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
1981-01	3181	5.0%
2017-02	1158	1.8%
2017-01	1015	1.6%
2017-05	794	1.3%
2016-10	768	1.2%
1994-06	663	1.0%
2013-10	533	0.8%
2002-11	503	0.8%
1995-04	478	0.8%
1994-07	438	0.7%
Other values (567)	53917	85.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_gender
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

1	32712
2	30985

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	2
4th row	1
5th row	2

Common Values

Value	Count	Frequency (%)
1	32712	51.4%
2	30985	48.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	32712	51.4%
2	30985	48.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_birth_date
Categorical

HIGH CARDINALITY

Distinct	1092
Distinct (%)	1.7%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

1952-03	132
1952-04	127
1966-05	125
1951-07	122
1956-07	122
Other values (1087)	63069

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	93 ?
Unique (%)	0.1%

Sample

1st row	1943-09
2nd row	1994-02
3rd row	1936-10
4th row	1946-09
5th row	1996-04

Common Values

Value	Count	Frequency (%)
1952-03	132	0.2%
1952-04	127	0.2%
1966-05	125	0.2%
1951-07	122	0.2%
1956-07	122	0.2%
1943-07	117	0.2%
1951-03	116	0.2%
1955-01	114	0.2%
1952-01	113	0.2%
1956-08	112	0.2%
Other values (1082)	62497	98.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
1952-03	132	0.2%
1952-04	127	0.2%
1966-05	125	0.2%
1951-07	122	0.2%
1956-07	122	0.2%
1943-07	117	0.2%
1951-03	116	0.2%
1955-01	114	0.2%
1952-01	113	0.2%
1956-08	112	0.2%
Other values (1082)	62497	98.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_postal_code
Real number (ℝ_≥0)

Distinct	1034
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5577.261959

Minimum	0
Maximum	9992
Zeros	10
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	1400
Q1	2650
median	4877
Q3	8750
95-th percentile	9810
Maximum	9992
Range	9992
Interquartile range (IQR)	6100

Descriptive statistics

Standard deviation	3020.064554
Coefficient of variation (CV)	0.5414959125
Kurtosis	-1.620230611
Mean	5577.261959
Median Absolute Deviation (MAD)	2817
Skewness	0.05374443809
Sum	355254855
Variance	9120789.908
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
9120	540	0.8%
9100	523	0.8%
8800	522	0.8%
2800	452	0.7%
2100	414	0.6%
9940	394	0.6%
2960	388	0.6%
8400	386	0.6%
3590	375	0.6%
3740	341	0.5%
Other values (1024)	59362	93.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	10	< 0.1%
1000	104	0.2%
1020	114	0.2%
1030	265	0.4%
1040	88	0.1%
1050	155	0.2%
1060	69	0.1%
1070	324	0.5%
1080	184	0.3%
1081	41	0.1%

Value	Count	Frequency (%)
9992	3	< 0.1%
9991	91	0.1%
9990	322	0.5%
9988	10	< 0.1%
9982	14	< 0.1%
9981	8	< 0.1%
9980	107	0.2%
9971	36	0.1%
9970	41	0.1%
9968	116	0.2%

customer_occupation_code
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING

Distinct	10
Distinct (%)	< 0.1%
Missing	2002
Missing (%)	3.1%
Infinite	0
Infinite (%)	0.0%
Mean	8.77353108

Minimum	0
Maximum	9
Zeros	421
Zeros (%)	0.7%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	9
Q1	9
median	9
Q3	9
95-th percentile	9
Maximum	9
Range	9
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.131452913
Coefficient of variation (CV)	0.128962091
Kurtosis	31.64044668
Mean	8.77353108
Median Absolute Deviation (MAD)	0
Skewness	-5.485065882
Sum	541283
Variance	1.280185695
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
9	58836	92.4%
4	1639	2.6%
0	421	0.7%
8	318	0.5%
6	183	0.3%
5	153	0.2%
7	104	0.2%
1	24	< 0.1%
3	10	< 0.1%
2	7	< 0.1%
(Missing)	2002	3.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	421	0.7%
1	24	< 0.1%
2	7	< 0.1%
3	10	< 0.1%
4	1639	2.6%
5	153	0.2%
6	183	0.3%
7	104	0.2%
8	318	0.5%
9	58836	92.4%

Value	Count	Frequency (%)
9	58836	92.4%
8	318	0.5%
7	104	0.2%
6	183	0.3%
5	153	0.2%
4	1639	2.6%
3	10	< 0.1%
2	7	< 0.1%
1	24	< 0.1%
0	421	0.7%

customer_self_employed
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	58154
1	5543

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	58154	91.3%
1	5543	8.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	58154	91.3%
1	5543	8.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_education
Real number (ℝ_≥0)

MISSING
ZEROS

Distinct	7
Distinct (%)	< 0.1%
Missing	47125
Missing (%)	74.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.463734009

Minimum	0
Maximum	6
Zeros	2178
Zeros (%)	3.4%
Negative	0
Negative (%)	0.0%
Memory size	497.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	2
Q3	3
95-th percentile	5
Maximum	6
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.520308961
Coefficient of variation (CV)	0.6170751206
Kurtosis	-0.4347524722
Mean	2.463734009
Median Absolute Deviation (MAD)	1
Skewness	0.2069922379
Sum	40829
Variance	2.311339336
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
3	5015	7.9%
2	4506	7.1%
0	2178	3.4%
5	2064	3.2%
1	1802	2.8%
4	696	1.1%
6	311	0.5%
(Missing)	47125	74.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2178	3.4%
1	1802	2.8%
2	4506	7.1%
3	5015	7.9%
4	696	1.1%
5	2064	3.2%
6	311	0.5%

Value	Count	Frequency (%)
6	311	0.5%
5	2064	3.2%
4	696	1.1%
3	5015	7.9%
2	4506	7.1%
1	1802	2.8%
0	2178	3.4%

customer_children
Categorical

HIGH CORRELATION
MISSING

Distinct	8
Distinct (%)	< 0.1%
Missing	23364
Missing (%)	36.7%
Memory size	497.8 KiB

no	22886
mature	4849
adolescent	3912
young	2652
preschool	2322
Other values (3)	3712

Length

Max length	10
Median length	2
Mean length	4.283737882
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	mature
2nd row	mature
3rd row	no
4th row	no
5th row	no

Common Values

Value	Count	Frequency (%)
no	22886	35.9%
mature	4849	7.6%
adolescent	3912	6.1%
young	2652	4.2%
preschool	2322	3.6%
grownup	1908	3.0%
onebaby	1466	2.3%
yes	338	0.5%
(Missing)	23364	36.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	22886	56.7%
mature	4849	12.0%
adolescent	3912	9.7%
young	2652	6.6%
preschool	2322	5.8%
grownup	1908	4.7%
onebaby	1466	3.6%
yes	338	0.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

customer_relationship
Categorical

HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	14899
Missing (%)	23.4%
Memory size	497.8 KiB

couple	36179
single	12619

Length

Max length	6
Median length	6
Mean length	6
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	couple
2nd row	single
3rd row	couple
4th row	couple
5th row	couple

Common Values

Value	Count	Frequency (%)
couple	36179	56.8%
single	12619	19.8%
(Missing)	14899	23.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
couple	36179	74.1%
single	12619	25.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

target
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	497.8 KiB

0	61784
1	1913

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	61784	97.0%
1	1913	3.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	61784	97.0%
1	1913	3.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	client_id	homebanking_active	has_homebanking	has_fire_car_other_insurance	has_current_account	has_savings_account	has_current_account_starter	prem_fire_car_other_insurance	bal_current_account	bal_savings_account	bal_current_account_starter	visits_distinct_so	visits_distinct_so_areas	customer_since_all	customer_since_bank	customer_gender	customer_birth_date	customer_postal_code	customer_occupation_code	customer_education	customer_children	customer_relationship	target
0	910df42ad36243aa4ce16324cd7b15b0	0	0	1	1	1	0	20	590	22000	0	1.0	1.0	1983-03	1994-08	1	1943-09	3630	9.0	0.0	NaN	NaN	0
1	4e19dc3a54323c5bbfc374664b950cd1	1	1	0	1	1	0	0	940	10570	0	1.0	1.0	2017-01	2017-01	1	1994-02	2460	9.0	NaN	mature	couple	0
2	f5d08db1b86c0cb0f566bf446cff1fb4	1	1	1	1	1	0	320	1210	15200	0	1.0	1.0	1980-12	1980-12	2	1936-10	2660	9.0	NaN	NaN	single	0
3	26170ecf63653e215c52f4262c1c4859	0	0	1	0	1	0	160	0	29020	0	1.0	1.0	1998-08	2013-10	1	1946-09	6600	9.0	NaN	NaN	NaN	0
4	c078009957dffb64f20e61b41220a976	0	0	0	0	1	0	0	0	13650	0	1.0	1.0	2012-11	2012-11	2	1996-04	8550	9.0	NaN	mature	couple	1
5	f7bae3a0fefd323ecf7d4a2fab4e7826	1	1	0	1	0	1	0	4150	0	4150	1.0	1.0	2004-08	2015-08	2	1963-11	1430	NaN	NaN	no	couple	0
6	e1b2293260bd55bc20700f122d70480f	0	0	0	0	1	0	0	0	48170	0	1.0	1.0	2012-03	2012-03	2	1950-10	2860	9.0	NaN	no	couple	0
7	2063dc028a989d9752b9565bd42a4eff	0	0	0	0	1	0	0	0	27080	0	1.0	1.0	1992-02	1992-02	2	1942-04	8570	9.0	NaN	no	couple	0
8	fbb7fcacec0c6a0199e0d012cbf63fed	0	0	0	0	1	0	0	0	9680	0	1.0	1.0	2006-10	2006-10	2	1998-06	9320	9.0	NaN	mature	couple	0
9	5c9b1a4b7b9255d5d9d9fac3328a26a4	0	0	1	1	1	0	70	13570	8850	0	1.0	1.0	1975-01	1996-07	1	1938-01	2220	9.0	NaN	no	couple	0

Last rows

	client_id	homebanking_active	has_homebanking	has_insurance_21	has_life_insurance_decreasing_cap	has_fire_car_other_insurance	has_mortgage_loan	has_current_account	has_savings_account	bal_insurance_21	cap_life_insurance_decreasing_cap	prem_fire_car_other_insurance	bal_mortgage_loan	bal_current_account	bal_savings_account	visits_distinct_so	visits_distinct_so_areas	customer_since_all	customer_since_bank	customer_gender	customer_birth_date	customer_postal_code	customer_occupation_code	customer_self_employed	customer_education	customer_children	customer_relationship
63687	02a8b338cecb9964a05b70ebd8494e14	1	1	0	0	1	0	1	1	0	0	250	0	5630	28760	2.0	1.0	2006-01	2006-06	2	1958-12	7170	9.0	0	NaN	NaN	NaN
63688	992c155cf2d7f13470f54151a8e3e192	0	0	0	0	1	0	0	1	0	0	390	0	0	20550	1.0	1.0	1980-08	2017-02	1	1948-04	3128	9.0	0	NaN	no	couple
63689	340a31bb1a3af30d7058e0b2c2fa4c26	0	0	0	0	0	0	0	1	0	0	0	0	0	6600	1.0	1.0	2008-12	2008-12	2	1935-02	4340	9.0	0	NaN	no	NaN
63690	3314ba6bf9afd34b76199b7f0a968895	0	0	0	0	0	0	0	1	0	0	0	0	0	25620	1.0	1.0	1981-01	1981-01	1	1963-07	2243	9.0	1	NaN	mature	couple
63691	7d13effc553a8e1a03f40470f8c3ae91	1	1	0	1	0	1	1	1	0	133000	0	162600	2980	16240	2.0	1.0	1997-06	1997-06	1	1980-03	8520	9.0	1	1.0	preschool	couple
63692	0a58f2eb841ddac0626dacac6ca69524	1	1	1	0	0	0	1	1	2420	0	0	0	80	17060	2.0	1.0	1998-06	1998-06	2	1982-02	8000	9.0	0	NaN	no	couple
63693	193be2222be99bf04f42193b5cdfb95d	0	0	1	0	1	1	1	1	7810	0	1180	13030	2690	22400	2.0	1.0	1989-05	1989-05	2	1965-05	2020	9.0	0	1.0	NaN	NaN
63694	fa9f074ec8cad610ccaec2270021490e	0	1	0	1	1	1	1	1	0	186000	520	179550	2920	6820	3.0	1.0	1991-01	1991-01	2	1976-11	1070	9.0	0	3.0	NaN	single
63695	5236064e97c655b0ad99ed2155e1139e	0	0	0	0	0	0	0	1	0	0	0	0	0	5690	1.0	1.0	2014-04	2014-04	1	1951-03	2560	9.0	0	NaN	no	couple
63696	977dda870c3f54df46297df3869b2907	1	1	0	0	0	0	1	1	0	0	0	0	6890	13540	1.0	1.0	2002-02	2002-02	1	1995-03	9100	9.0	0	NaN	no	NaN

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks